Linear adaptive equalizers having a plurality of taps are widely used in digital communication receivers in order to provide correction for multipath channel distortion. Adaptive algorithms, such as the least mean squares (LMS) algorithm, are typically implemented in order to determine the weight values for the taps of the equalizer. Such adaptive algorithms are easy to implement and provide reasonably good performance. However, under difficult channel conditions, these algorithms may fail to provide tap weights that converge to the desired values.
It is well known that this failure may be avoided if the tap weights, instead of being initialized to values of zero as is often done, are initialized at least somewhat close to their final desired values based on a knowledge of the impulse response of the channel. An estimate of the channel impulse response (CIR) may be derived from an a priori known training sequence periodically transmitted prior to, and/or along with, the unknown data. One such system with this feature is specified in the ATSC 8VSB standard for digital terrestrial television broadcasting.
The channel impulse response is typically estimated in a receiver by cross-correlating the training sequence as received with a representation of the known transmitted training sequence stored in the receiver as the reference. The Z-transform of the estimated channel impulse response is derived and inverted. From the inverted Z-transform, a vector is formed having a plurality of elements, and these elements are used to initialize a corresponding number of tap weights of the equalizer.
A conventional linear adaptive equalizer 10 that utilizes a transversal filter 12 is shown in FIG. 1. The transversal filter 12 comprises a plurality of taps Nff whose weights are applied to the received signal in order to eliminate the effects of multipath from the received signal. The transversal filter 12 includes a plurality of outputs 141 through 14n and a corresponding plurality of multipliers 161 through 16n. The signal on each of the outputs 141 through 14n is multiplied by a corresponding tap weight from a conventional tap weight update algorithm 18 (such as an LMS) by a corresponding one of the multipliers 161 through 16n. The outputs from the multipliers 161 through 16n are added together by an adder 20, and the output from the adder 20 is supplied as an output of the conventional linear adaptive equalizer 10.
The output from the adder 20 is also supplied to a decision directed/blind module 22 that compares the filter output with either the known training signal, when the known training signal is being received, or likely corrected data decisions, when the unknown data instead of the known training signal are being received. This comparison forms an error signal e that is used by the conventional tap weight update algorithm 18 to update the linear tap weights so as to minimize the value of the error e.
During training, the conventional tap weight update algorithm 18 typically estimates the channel impulse response by a-periodically cross-correlating the training sequence as received with a stored version of the known training sequence. If s [k] is defined as the stored known training sequence for k=0 . . . (L−1), and if x [k] is defined as the received signal sampled at the symbol rate, with x [0] being the first received training symbol in the received signal, the cross-correlation is given by the following equation:
                                          h            ⁡                          [              m              ]                                =                                    ∑                              k                =                0                                            L                -                1                                      ⁢                                                  ⁢                                          s                ⁡                                  [                  k                  ]                                            ⁢                              x                ⁡                                  [                                      k                    +                    m                                    ]                                                                    ,                                  ⁢                              for            ⁢                                                  -                          L              chan                                ≤          m          ≤                      L            chan                                              (        1        )            where Lchan is the length of the channel and is typically set at 576.
The conventional tap weight update algorithm 18 then determines the Z-transform of h [m] and inverts the Z-transform in order to determine the tap weights that are supplied to the multipliers 161 through 16n.
This algorithm addresses channel related noise. However, there are other sources of noise. These other noise sources may, in a general, be described as deterministic noise and non-deterministic noise. Deterministic noise is noise that is known a priori. An example of deterministic noise is noise due to the finiteness of the cross-correlation as described in copending U.S. patent application Ser. No. 10/142,108 filed on May 9, 2002 and in copending U.S. patent application Ser. No. 10/142,110 filed on May 9, 2002.
As described in these applications, noise due to the finiteness of the cross-correlation may be determined by a-periodically cross-correlating a known training sequence with a received training sequence to produce a cross-correlation vector, by estimating a correction vector related to the finiteness noise component, and by iteratively subtracting truncated representations of the correction vector from the cross-correlation vector so as to produce a succession of cross-correlation outputs of increasing accuracy.
After the deterministic noise is removed from the channel impulse response, however, the channel impulse response still contains a noise component referred to herein as non-deterministic noise. The present invention is directed to the suppression of this non-deterministic noise from the channel impulse response.